Quantitative Analysis for Scanning Electron Microscopy Images of DU-Silicide

Abstract

This project explores advanced image analysis techniques to assess the microstructure of depleted uranium fuel compacts before and after undergoing deformation in a rolling mill. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract key quantitative metrics, including fuel plate thickness, particle size distribution, and material composition from scanning electron microscope (SEM) images. Additionally, uncertainty quantification methods will be applied to evaluate measurement accuracy. By automating feature extraction and material classification, this study contributes to enhancing the precision of SEM-based material characterization, which is critical for nuclear fuel research and reactor conversion programs